Image processing apparatus, method, and storage medium for performing soft proof processing

ABSTRACT

According to one aspect of the invention, an image processing apparatus includes: a lowpass filter generation unit configured to generate a lowpass filter corresponding to gonio-spectral reflection characteristics; a map generation unit configured to perform filter processing on intensity distribution of observation illumination in order to generate a map indicating a capture intensity distribution of observation illumination for a representative color; an intensity calculation unit configured to interpolate a capture intensity at a position corresponding to a pixel in the map in order to calculate a capture intensity of the observation illumination for the pixel; and a proofing color calculation unit configured to multiply the difference between a glossy component and a diffuse component by the capture intensity to calculate a proofing glossy component, and to add the diffuse component to the proofing glossy component in order to calculate a proofing color of the pixel.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to an image processing apparatus and imageprocessing method which perform soft proof processing of reproducing, ona monitor, a print product under observation illumination.

2. Description of the Related Art

Processing of using, for example, a PC (Personal Computer), to simulatethe finish quality of an actual print product obtained by, for example,a printer, and displaying an image obtained by simulation is called softproof processing. It is a common practice in soft proof processing toperform color matching processing for color components (to be referredto as diffuse components hereinafter) of light reflected by an actualprint product, and faithfully reproduce their tonalities on a displaydevice. In the recent soft proof processing, a technique of using CG(Computer Graphics) to simulate not only diffuse components of a printproduct but also glossy components (illumination reflection) of theprint product is becoming prevalent.

To precisely reproduce (proof) glossy components of a target printproduct, it is necessary to reflect its gonio-spectral reflectioncharacteristics on soft proof processing. The gonio-spectral reflectioncharacteristics mean herein characteristics indicating the angle andintensity of reflection of light upon irradiation, and are referred toas a BRDF (Bidirection Reflectance Distribution Function). Especially ina pigment printer that has gained a large market share in recent years,the gonio-spectral reflection characteristics change for each colorbecause the shape of the medium surface changes in accordance with, forexample, the print density and the order of ink landing. Hence, it isnecessary to precisely reflect the gonio-spectral reflectioncharacteristics of each color on soft proof processing for a pigmentprinter.

Conventionally, in the CG field, the glossy components are representedusing, for example, a Phong model obtained by approximating thegonio-spectral reflection characteristics. Also, to more accuratelyreflect the gonio-spectral reflection characteristics, a technique ofcombining a plurality of CG models to approximate the gonio-spectralreflection characteristics of the target has also been proposed (see,for example, Japanese Patent Laid-Open No. 2004-126692).

The CG model that is conventionally introduced to represent the glossycomponents generates an error with respect to actual gonio-spectralreflection characteristics. This makes it impossible to preciselyrepresent the glossy components. Also, the technique of approximating aplurality of CG models to more accurately reflect the gonio-spectralreflection characteristics of the target can more accurately reflectspecific gonio-spectral reflection characteristics on soft proofprocessing but nonetheless cannot cope with a situation in which thegonio-spectral reflection characteristics differ for each color.

SUMMARY OF THE INVENTION

The present invention has been made in order to solve theabove-mentioned problems, and provides a technique of accuratelyreproducing glossy components of a print product subjected toobservation illumination by precisely reflecting the gonio-spectralreflection characteristics of the print product, that differ for eachcolor, on soft proof processing of reproducing the print product on amonitor.

According to one aspect of the invention, an image processing apparatuswhich performs soft proof processing of reproducing, on a monitor, aprint product under observation illumination, comprises: acharacteristics holding unit configured to hold, specular reflection ofthe observation illumination at a sample print product having aplurality of colors printed thereon as color-specific glossy components,diffuse reflection of the observation illumination at the sample printproduct as color-specific diffuse components, and color-specificgonio-spectral reflection characteristics at the sample print product;an evaluation value holding unit configured to hold, an evaluation valuefor each of the held color-specific gonio-spectral reflectioncharacteristics; an intensity distribution holding unit configured tohold an intensity distribution of the observation illumination; alowpass filter generation unit configured to obtain the gonio-spectralreflection characteristics for each of representative colors from thecharacteristics holding unit, and to generate a lowpass filtercorresponding to the gonio-spectral reflection characteristics for eachof the representative colors, where the representative colorscorresponding to the evaluation values held in the evaluation valueholding unit; a map generation unit configured to perform filterprocessing on the intensity distribution of the observation illuminationheld in the intensity distribution holding unit, for each of therepresentative colors, by means of the lowpass filter corresponding tothe representative color, in order to generate a map indicating acapture intensity distribution of observation illumination for therepresentative color; an intensity calculation unit configured to obtainan evaluation value corresponding to a color of a pixel in aproofing-target image from the evaluation value holding unit, and tointerpolate a capture intensity at a position corresponding to the pixelin the map for each of the representative colors, based on theevaluation value, in order to calculate a capture intensity of theobservation illumination for the pixel, for each pixel in theproofing-target image; a proofing color calculation unit configured toobtain the glossy component and the diffuse component corresponding to acolor of a pixel in the proofing-target image from the characteristicsholding unit, to multiply the difference between the glossy componentand the diffuse component by the capture intensity to calculate aproofing glossy component, and to add the diffuse component to theproofing glossy component in order to calculate a proofing color of thepixel, for each pixel in the proofing-target image; and a display imagegeneration unit configured to convert the proofing color into a signalvalue for the monitor to generate a display image, for each pixel in theproofing-target image.

According to another aspect of the invention, an image processingapparatus which generates a proofed image used to reproduce, on amonitor, a print product under observation illumination, comprises: acharacteristics holding unit configured to hold, specular reflection ofthe observation illumination at a sample print product having aplurality of colors printed thereon as color-specific glossy components,diffuse reflection of the observation illumination at the sample printproduct as color-specific diffuse components, and color-specificgonio-spectral reflection characteristics at the sample print product;an evaluation value holding unit configured to hold, an evaluation valuefor each of the held color-specific gonio-spectral reflectioncharacteristics; an intensity distribution holding unit configured tohold an intensity distribution of the observation illumination; acharacteristics calculation unit configured to obtain the gonio-spectralreflection characteristics for each of representative colors from thegloss characteristics holding unit, and to calculate gonio-spectralreflection characteristics for a color of each pixel in theproofing-target image by means of obtaining the evaluation valuecorresponding to the color of the pixel from the evaluation valueholding unit, and interpolating the gonio-spectral reflectioncharacteristics for each of the representative colors, based on theevaluation value, where the representative colors corresponding to theevaluation values held in the evaluation value holding unit a lowpassfilter generation unit configured to generate a lowpass filtercorresponding to the gonio-spectral reflection characteristics for acolor of each pixel in the proofing-target image; an intensitycalculation unit configured to perform filter processing on theintensity distribution of the observation illumination held in theillumination intensity distribution holding unit, by means of thelowpass filter corresponding to a color of a pixel in theproofing-target image, in order to calculate a capture intensitydistribution of the observation illumination for the pixel, for eachpixel in the proofing-target image; a proofing color calculation unitconfigured to obtain the glossy component and the diffuse componentcorresponding to a color of a pixel in the proofing-target image fromthe characteristics holding unit, to multiply the difference between theglossy component and the diffuse component by the capture intensity tocalculate a proofing glossy component, and to add the diffuse componentto the proofing glossy component in order to calculate a proofing colorof the pixel, for each pixel in the proofing-target image; and a displayimage generation unit configured to convert the proofing color into asignal value for the monitor to generate a display image, for each pixelin the proofing-target image.

According to still another aspect of the invention, an image processingapparatus which generates a proofed image used to reproduce, on amonitor, a print product under observation illumination, comprises: acharacteristics holding unit configured to hold, specular reflection ofthe observation illumination at a sample print product having aplurality of colors printed thereon as color-specific glossy components,diffuse reflection of the observation illumination at the sample printproduct as color-specific diffuse components, and color-specificgonio-spectral reflection characteristics at the sample print product;an intensity distribution holding unit configured to hold an intensitydistribution of the observation illumination; a lowpass filtergeneration unit configured to obtain the gonio-spectral reflectioncharacteristics for each of representative colors from the glosscharacteristics holding unit, and to generate a lowpass filtercorresponding to the gonio-spectral reflection characteristics, wherethe representative colors includes a plurality of colors in a colorregion of a proofing-target image; a map generation unit configured toperform filter processing on the intensity distribution of theobservation illumination held in the intensity distribution holding unitby means of the lowpass filter, in order to generate a map indicating acapture intensity distribution of observation illumination for therepresentative color, and to generate a table indicating a relationshipbetween the representative color and the map, for each of therepresentative colors; an intensity calculation unit configured tocalculate a capture intensity of the observation illuminationcorresponding to a position and a color of a pixel in theproofing-target image by looking up the table, for each pixel in theproofing-target image; a proofing color calculation unit configured toobtain the glossy component and the diffuse component corresponding to acolor of a pixel in the proofing-target image from the characteristicsholding unit, to multiply the difference between the glossy componentand the diffuse component by the capture intensity to calculate aproofing glossy component, and to add the diffuse component to theproofing glossy component in order to calculate a proofing color of thepixel, for each pixel in the proofing-target image; and a display imagegeneration unit configured to convert the proofing color into a signalvalue for the monitor to generate a display image, for each pixel in theproofing-target image.

According to yet another aspect of the invention, an image processingmethod is provided for an image processing apparatus comprising acharacteristics holding unit, an evaluation value holding unit, alowpass filter generation unit, a map generation unit, an intensitycalculation unit, a proofing color calculation unit, and a display imagegeneration unit, wherein the image processing apparatus performs softproof processing of reproducing, on a monitor, a print product underobservation illumination, wherein: the characteristics holding unit isconfigured to hold, specular reflection of the observation illuminationas color-specific glossy components, diffuse reflection of theobservation illumination as color-specific diffuse components, andcolor-specific gonio-spectral reflection characteristics; the evaluationvalue holding unit is configured to hold, an evaluation value for eachof the held color-specific gonio-spectral reflection characteristics;and the intensity distribution holding unit is configured to hold anintensity distribution of the observation illumination; the methodcomprising: a lowpass filter generation step of obtaining thegonio-spectral reflection characteristics for each of representativecolors from the characteristics holding unit, and generating a lowpassfilter corresponding to the gonio-spectral reflection characteristicsfor each of the representative colors, where the representative colorscorresponding to the evaluation values held in the evaluation valueholding unit; a map generation step of performing filter processing onthe intensity distribution of the observation illumination held in theintensity distribution holding unit, for each of the representativecolors, by means of the lowpass filter corresponding to therepresentative color, in order to generate a map indicating a captureintensity distribution of observation illumination for therepresentative color; an intensity calculation step of obtaining anevaluation value corresponding to a color of a pixel in aproofing-target image from the evaluation value holding unit, andinterpolating a capture intensity at a position corresponding to thepixel in the map for each of the representative colors, based on theevaluation value, in order to calculate a capture intensity of theobservation illumination for the pixel, for each pixel in theproofing-target image; a proofing color calculation step of obtainingthe glossy component and the diffuse component corresponding to a colorof a pixel in the proofing-target image from the characteristics holdingunit, multiplying the difference between the glossy component and thediffuse component by the capture intensity to calculate a proofingglossy component, and adding the diffuse component to the proofingglossy component in order to calculate a proofing color of the pixel,for each pixel in the proofing-target image; and a display imagegeneration step of converting the proofing color into a signal value forthe monitor to generate a display image, for each pixel in theproofing-target image.

The present invention enables accurately reproducing glossy componentsof a print product subjected to observation illumination by preciselyreflecting the gonio-spectral reflection characteristics of the printproduct, that differ for each color, on soft proof processing ofreproducing the print product on a monitor.

Further features of the present invention will become apparent from thefollowing description of exemplary embodiments with reference to theattached drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing the system configuration of an imageprocessing apparatus in the first embodiment;

FIG. 2 is a block diagram showing a functional configuration in thefirst embodiment;

FIG. 3 is a table showing the gonio-spectral reflection characteristicsof each color of an actual print product in the first embodiment;

FIG. 4 is a view showing a method of obtaining the gonio-spectralreflection characteristics in the first embodiment;

FIG. 5 is a flowchart showing image processing in the first embodiment;

FIG. 6 is a view illustrating an example of a virtual environment in thefirst embodiment;

FIG. 7 is a view illustrating an example of an illumination intensitydistribution in the first embodiment;

FIG. 8 is a view showing an overview of a reflection model under thevirtual environment in the first embodiment;

FIG. 9 is a flowchart showing representative color capture intensity mapcalculation processing in the first embodiment;

FIGS. 10A and 10B are views illustrating examples of representativecolor capture intensity maps in the first embodiment;

FIG. 11 is a flowchart showing capture intensity calculation processingin the first embodiment;

FIG. 12 is a table illustrating an example of an image clarityevaluation value LUT in the first embodiment;

FIG. 13 is a flowchart showing proofing color calculation processing inthe first embodiment;

FIGS. 14A and 14B are tables illustrating examples of a diffusion LUTand a gloss LUT in the first embodiment;

FIG. 15 is a block diagram showing a functional configuration in thesecond embodiment;

FIG. 16 is a flowchart showing image processing in the secondembodiment;

FIG. 17 is a flowchart showing target color gonio-spectral reflectioncharacteristics obtaining processing in the second embodiment;

FIG. 18 is a block diagram showing a functional configuration in thethird embodiment;

FIG. 19 is a flowchart showing image processing in the third embodiment;and

FIG. 20 is a table illustrating an example of a representative colorcapture intensity map LUT in the third embodiment.

DESCRIPTION OF THE EMBODIMENTS

Embodiments of the present invention will be described in detail belowwith reference to the accompanying drawings. Note that the followingembodiments do not limit the present invention defined by the scope ofthe claims, and not all combinations of features to be described inthese embodiments are indispensable for the present invention.

First Embodiment

System Configuration

FIG. 1 shows the system configuration of an image processing apparatuswhich performs soft proof processing in the first embodiment. Referringto FIG. 1, an input unit 101 is a device which inputs data and aninstruction from the user, and includes a keyboard and a pointing systemsuch as a mouse. A display unit 102 is a display device (monitor) whichdisplays, for example, a GUI, and uses, for example, a CRT or a liquidcrystal display. A data storage unit 103 is a device which stores imagedata and a program, and generally uses a hard disk. A CPU 104 controlsall types of processing in the above-mentioned respectiveconfigurations. A ROM 105 and a RAM 106 provide the CPU 104 with, forexample, a program, data, and the necessary working area for processingin the CPU 104. If a control program necessary for processing in the CPU104 is stored in the data storage unit 103 or ROM 105, it is executedupon being read into the RAM 106. However, if a program is received bythe apparatus via a communication unit 107, it is executed upon beingrecorded in the data storage unit 103 and then read into the RAM 106 orbeing directly read from the communication unit 107 into the RAM 106.The communication unit 107 is an interface (I/F) used for communicationbetween individual devices, and can operate in accordance with a knowncommunication scheme such as Ethernet®, USB, IEEE, or Bluetooth.Although the system configuration includes various constituent elementsother than those described above, a description thereof is not aprincipal part of the present invention and therefore will not be given.

Overview of Image Processing

FIG. 2 is a block diagram showing image processing executed by the imageprocessing apparatus in the first embodiment and the flow of data, thatis, a functional configuration in this embodiment. The image processingapparatus in this embodiment performs soft proof processing ofaccurately reproducing a print product of image data to be printed(proofing-target image) on a monitor so as to reproduce its diffusecomponents including glossy components.

In this embodiment, first, a virtual environment generation unit 202obtains an intensity distribution of illumination (to be referred to asobservation illumination hereinafter) used to observe a print product(to be referred to as a proofing-target print product hereinafter) of aproofing-target image from an illumination intensity distributionholding unit 201. An environment (to be referred to as a virtualenvironment hereinafter) used to virtually observe the print product isgenerated by CG based on the obtained intensity distribution and virtualenvironment information designated via the input unit 101 by the user.

A representative color lowpass filter generation unit 203 obtainsgonio-spectral reflection characteristics for a representative printcolor (to be referred to as a representative color hereinafter), whichare held in a representative color gonio-spectral reflectioncharacteristics holding unit 204 in advance, and generates a lowpassfilter based on the obtained gonio-spectral reflection characteristics.The gonio-spectral reflection characteristics (BRDF) of a representativecolor mean herein data (BRDF(θ)) obtained by measuring in advance thereflection intensity (X, Y, and Z values) of an actual print productupon light irradiation at each emergent angle (θ) for the RGB value ofeach representative color defined on the actual print product, as shownin FIG. 3. Note that two colors are used as representative colors inthis embodiment.

FIG. 4 illustrates an example of a BRDF measurement method. Referring toFIG. 4, an actual print product 302 of a sample image having a pluralityof color patches defined in the RGB space is irradiated with light usingactual illumination 301, and light reflected at each emergent angle θwith respect to incident light is measured using a goniometer 303. Theactual illumination 301 used to irradiate the actual print product 302is not limited to a specific one as long as reflection characteristicsfor each angle on the actual print product 302 can be obtained.

Although gonio-spectral reflection characteristics BRDF are representedby X, Y, and values in this embodiment, as shown in FIG. 3, they may berepresented using, for example, Lab values defined in an L*a*b* colorspace. Also, although gonio-spectral reflection characteristics BRDF(θ)corresponding to the emergent angle θ are measured upon fixing theincident angle at 45° in the example shown in FIG. 3, the measured valueof gonio-spectral reflection characteristics BRDF when the incidentangle is variable may be held in the representative color gonio-spectralreflection characteristics holding unit 204. Note that in the lattercase, gonio-spectral reflection characteristics BRDF(θ) described byequation (2) (to be described later) use a bivariate function defined bythe incident angle and the emergent angle.

A representative color capture intensity map calculation unit 205performs filter processing, which uses the lowpass filter generated bythe representative color lowpass filter generation unit 203, for theintensity distribution (observation illumination image) of observationillumination under the virtual environment generated by the virtualenvironment generation unit 202. With this filter processing, arepresentative color capture intensity map indicating the captureintensity of the observation illumination for the representative colorof the proofing-target print product is calculated. The representativecolor capture intensity map is obtained by blurring the intensitydistribution of the observation illumination in accordance with thegonio-spectral reflection characteristics of the representative color.

A virtual environment information obtaining unit 211 obtains specularreflection vector information in the virtual line-of-sight direction,which is calculated based on the virtual environment information inputto the virtual environment generation unit 202. After that, a captureintensity calculation unit 210 calculates the capture intensity of theobservation illumination for each pixel in the proofing-target imageheld in a proofing-target image holding unit 206. This calculation isdone for each pixel in the proofing-target image using therepresentative color capture intensity map obtained by therepresentative color capture intensity map calculation unit 205, thespecular reflection vector information obtained by the virtualenvironment information obtaining unit 211, and an image clarityevaluation value held for each color in an image clarity evaluationvalue LUT 209. The image clarity evaluation value means herein a valuewhich has a high correlation with the gonio-spectral reflectioncharacteristics BRDF held in the representative color gonio-spectralreflection characteristics holding unit 204, and is measured in advancefor each color. In this embodiment, the full width at half maximum ofthe gonio-spectral reflection characteristics BRDF is held in advance inthe image clarity evaluation value LUT 209 as an image clarityevaluation value indicating their variance.

A proofing color calculation unit 212 calculates a proofing color foreach pixel in the proofing-target image. In this case, a proofing coloris calculated using a glossy component and a diffuse component which areheld for each color in a gloss LUT 207 and a diffusion LUT 208,respectively, and the capture intensity obtained by the captureintensity calculation unit 210.

In the above-mentioned way, when proofing colors for all pixels in theproofing-target image are calculated, they are converted into RGB valuesfor monitor display and stored in a proofed image holding unit 213.Lastly, a display image generation unit 214 generates an image to bedisplayed on a monitor from the proofed image stored in the proofedimage holding unit 213, in accordance with the vector informationobtained by the virtual environment information obtaining unit 211, anda display instruction from the user. In this case, the monitor is thedisplay unit 102.

Details of Image Processing

Image processing in the first embodiment will be described in detailbelow with reference to a flowchart shown in FIG. 5. Note that the CPU104 controls image processing in this embodiment, as described above.

First, in step S1001, the virtual environment generation unit 202generates an environment, used to virtually observe a print product, bymeans of CG based on virtual environment information designated via theinput unit 101. More specifically, first, 3D objects such as a wall, aceiling, and a floor, as shown in FIG. 6, are set to generate a virtualspace 401. Next, virtual illumination 402 used to observe a virtualprint product 403 is set, the virtual print product 403 is set near thecenter of the virtual space, and the position of a virtual point ofsight 404 is set finally.

In this embodiment, an illumination intensity distribution obtained by,for example, a colorimeter for observation illumination under anenvironment (actual environment) used to actually observe aproofing-target print product is set as the virtual illumination 402.This illumination intensity distribution is data obtained by measuringthe emission intensity of observation illumination on a two-dimensionalplane (for example, the ceiling surface on which the virtualillumination 402 is set in the virtual space 401), and is held in theillumination intensity distribution holding unit 201 as two-dimensionaldata as shown in, for example, FIG. 7. Illumination intensitydistribution data on a two-dimensional plane, which serves as thevirtual illumination 402, will be referred to as an observationillumination image hereinafter. Although an example in whichtwo-dimensional data (observation illumination image) on a given planeis used as an illumination intensity distribution will be given in thisembodiment, the data form is not limited to a specific one as long asthe position coordinates on the given plane and the illuminationintensity can maintain a given relationship. Also, although an examplein which an actually measured illumination intensity distribution isused has been given in order to more precisely proof glossy components,preset data held in advance in the CG model, for example, can also beused. Moreover, intensity distributions for a plurality of types ofillumination may be held in the illumination intensity distributionholding unit 201, and an intensity distribution for observationillumination set as the virtual illumination 402 may be selectively usedin this case.

In step S1002, the representative color lowpass filter generation unit203 generates a lowpass filter for a representative color. Morespecifically, first, the gonio-spectral reflection characteristicsBRDF(θ) of a representative color are read from the representative colorgonio-spectral reflection characteristics holding unit 204. Therepresentative color means herein two colors: a color which captures theobservation illumination (gloss) the most clearly on the print product,and a color which captures the observation illumination the leastclearly on the print product, that is, two colors having the maximum andminimum color-specific image clarity evaluation values, respectively,measured in advance. Next, a distance Dis [pixel] between the virtualillumination 402 and the virtual print product 403 under the virtualenvironment is obtained, and each emergent angle θ of the gonio-spectralreflection characteristics BRDF(θ) for each representative color isconverted into a pixel count Pix indicating the distance from theirradiation point in BRDF measurement. This conversion is done inaccordance with:Pix=Dis×tan θ for 0°≦θ≦45°  (1)Note that the θ range corresponds to the range of the emergent angle ofthe gonio-spectral reflection characteristics BRDF(θ), and is, forexample, the range of 0° to 45° in the example shown in FIG. 3.

After that, based on the gonio-spectral reflection characteristicsBRDF(Pix) for each pixel count Pix, a two-dimensional lowpass filterLPF(a,b) for each representative color is generated in accordance with:LPF(a,b)=BRDF((a ² +b ²)^(1/2))for −S≦a≦S, −S≦b≦SS=Dis×tan 45°(a ² +b ²)^(1/2) =S when (a ² +b ²)^(1/2) ≧S  (2)where a and b are coordinates indicating a position having, as itsorigin, the irradiation point on the virtual print product 403 in BRDFmeasurement, and S is a parameter indicating the filter size andcorresponds to the value Pix at a maximum emergent angle (45°) inequation (1).

In step S1003, the representative color capture intensity mapcalculation unit 205 performs filter processing for each representativecolor for the observation illumination image formed by the virtualillumination 402, using the lowpass filter generated for eachrepresentative color in step S1002. Thus, a representative color captureintensity map is generated by blurring the luminance distribution of thevirtual illumination 402 for each representative color in accordancewith the gonio-spectral reflection characteristics of thisrepresentative color. Processing in step S1003 will be described in moredetail later.

In step S1004, the proofing-target image holding unit 206 obtains theRGB value at a pixel position n in a proofing-target image. Note thatthe pixel position n is initialized (to, for example, 1) beforeprocessing in step S1004 for the first time. The RGB value at the pixelposition n in the proofing-target image will simply be referred to asthe RGB value hereinafter.

In step S1005, first, the virtual environment information obtaining unit211 obtains position information regarding the virtual print product 403corresponding to the pixel position n in the proofing-target image, inaccordance with the virtual environment information referred to in stepS1001 as well. A specular reflection vector R for a line-of-sight vectorE in the glossy reflection model shown in FIG. 8 is calculated for theobtained position. This calculation is done in accordance with a glossycomponent expression based on the glossy reflection model shown in FIG.8 as given by:R=−E+2(N·E)N  (3)

As shown in FIG. 8, N is a vector indicating the normal direction to thesurface of the virtual print product 403, and is obtained from theinformation of the virtual print product 403 designated by the user; Eis a vector indicating the line-of-sight direction of the virtual pointof sight 404 under the virtual environment; R is a vector indicating thespecular reflection direction of the vector E; and (N·E) is the innerproduct of the vectors N and E.

In step S1006, the capture intensity calculation unit 210 calculates thecapture intensity of the virtual illumination 402 at the pixel positionn in the proofing-target image (that is, on the virtual print product403). First, an image clarity evaluation value for the RGB value at thepixel position n is obtained from the image clarity evaluation value LUT209. Note that the image clarity evaluation value means herein the fullwidth at half maximum of the gonio-spectral reflection characteristicsBRDF indicating their variance, as described above. The captureintensity of the virtual illumination 402 for the RGB value at the pixelposition n is calculated using the obtained image clarity evaluationvalue and the representative color capture intensity map calculated instep S1003. Processing in step S1006 will be described in more detaillater.

In step S1007, the proofing color calculation unit 212 calculates aproofing color for the pixel position n in the proofing-target imageusing the gloss LUT 207, the diffusion LUT 208, and the captureintensity calculated in step S1006. Processing in step S1007 will bedescribed in more detail later.

In step S1008, it is determined whether proofing color calculation iscomplete upon processing in steps S1005 to S1007 for the total number ofpixels N in the proofing-target image, that is, whether the pixelposition n that is currently being processed in the proofing-targetimage. If n=N, the process advances to step S1010; or if n≠N, theprocess advances to step S1009, in which the pixel position n isincremented, and the process returns to step S1004.

In step S1010, the proofing color calculation unit 212 converts theproofing color calculated in step S1007 into a signal value for monitordisplay, and stores it in the proofed image holding unit 213 as aproofed image, for all pixels in the proofing-target image. In thiscase, the proofing color is converted into a signal value for monitordisplay by means of, for example, a conversion formula from an XYZ value(X_(out), Y_(out), Z_(out)) of the proofed image into an sRGB value(R_(out), G_(out), B_(out)) as presented in:

$\begin{matrix}{{\begin{pmatrix}R_{{out}\_{Linear}} \\G_{{out}\_{Linear}} \\B_{{out}\_{Linear}}\end{pmatrix} = {\begin{pmatrix}3.241 & {- 1.537} & {- 0.499} \\{- 0.969} & 1.876 & 0.042 \\0.056 & {- 0.204} & 1.057\end{pmatrix}\begin{pmatrix}X_{out} \\Y_{out} \\Z_{out}\end{pmatrix}}},{\begin{pmatrix}R_{out} \\G_{out} \\B_{out}\end{pmatrix} = \begin{pmatrix}R_{{out}\_{Linear}}^{0.45} \\G_{{out}\_{Linear}}^{0.45} \\B_{{out}\_{Linear}}^{0.45}\end{pmatrix}}} & (4)\end{matrix}$

Lastly, in step S1011, the display image generation unit 214 generates adisplay image of the proofed image held in the proofed image holdingunit 213, in accordance with an instruction for image processing such asenlargement/reduction/rotation/shift processing, which is input from theinput unit 101 by the user. That is, a coordinate position (x_(in),y_(in), z_(in)) of the proofed image indicated as the position of thevirtual print product 403 in the virtual environment informationobtained by the virtual environment information obtaining unit 211 isconverted into a coordinate position (x_(out), y_(out), z_(out)) fordrawing corresponding to a user instruction, and the process ends.

Representative Color Capture Intensity Map Calculation Processing(S1003)

Representative color capture intensity map calculation processing instep S1003 will be described below with reference to a flowchart shownin FIG. 9.

First, in step S1101, a variable k indicating the number of a lowpassfilter (that is, the number of a representative color) is initialized to1 in step S1101, and a kth lowpass filter LPF_(k) is obtained in stepS1102. In step S1103, a discrete convolution operation between aluminance LUM(i,j) of the virtual illumination 402 and the kth lowpassfilter LPF_(k) for the kth representative color is performed tocalculate a capture intensity lum_(k)(i,j) for this representativecolor. This operation is done in accordance with:

$\begin{matrix}{{{{lum}_{k}\left( {i,j} \right)} = {\sum\limits_{a = {- S}}^{S}{\sum\limits_{b = {- S}}^{S}{{{LUM}\left( {{i - a},{j - b}} \right)}{{LPF}_{k}\left( {a,b} \right)}}}}}{{i = 0},\ldots\mspace{14mu},{M - 1},{j = 0},\ldots\mspace{14mu},{N - 1}}} & (5)\end{matrix}$where (i,j) are the position coordinates in the illumination imageformed by the virtual illumination 402, and M and N are the maximumvalues of the position coordinates i and j, respectively.

In step S1104, the capture intensity map lum_(k) calculated in stepS1103 is normalized to calculate a representative color captureintensity map LUM_(k) for the number k of a representative color inaccordance with:LUM_(k)(i,j)={lum_(k)(i,j)}/{max_lum_(k)}  (6)where max_lum_(k) is the maximum value of the capture intensity maplum_(k) for the number k of a representative color.

In step S1105, it is determined whether processing is complete for thetotal number of lowpass filters K. If processing is complete, that is,k=K, processing in step S1003 ends; or if k≠K, the process advances tostep S1106, in which the number k of a lowpass filter is incremented,and the process returns to step S1102.

FIGS. 10A and 10B illustrate examples of representative color captureintensity maps obtained for the observation illumination image(illumination intensity distribution) shown in FIG. 7. Therepresentative color capture intensity map is two-dimensional dataobtained by holding, on a two-dimensional plane, the luminance of theobservation illumination captured by each representative color, and iscalculated as an observation illumination image, as shown in FIGS. 10Aand 10B. FIG. 10A shows a representative color capture intensity mapcalculated using a lowpass filter generated for a color which capturesthe observation illumination the most clearly, that is, a color having amaximum image clarity evaluation value. Also, FIG. 10B shows arepresentative color capture intensity map calculated using a lowpassfilter generated for a color which captures observation illumination theleast clearly, that is, a color with a minimum image clarity evaluationvalue. That is, FIG. 10A shows a capture intensity map with a highestclarity, and FIG. 10B shows a capture intensity map with a lowestclarity. Note that as described above, the observation illuminationimage need not always be data having a value for every coordinateposition in a two-dimensional plane, and its shape is not limited to aspecific one as long as it is held in the illumination intensitydistribution holding unit 201. A value for two-dimensional coordinatesneed only be held for the representative color capture intensity map aswell.

Capture Intensity Calculation Processing (S1006)

Capture intensity calculation processing in step S1006 will be describedin detail below with reference to a flowchart shown in FIG. 11.

First, in step S1201, a value indicating the full width at half maximumof the gonio-spectral reflection characteristics BRDF is obtained fromthe image clarity evaluation value LUT 209 as an image clarityevaluation value H corresponding to the input pixel value (RGB) of theproofing-target image. FIG. 12 illustrates an example of the imageclarity evaluation value LUT 209. In the image clarity evaluation valueLUT 209, a plurality of RGB values are associated with the image clarityevaluation values H, as shown in FIG. 12. Hence, to obtain acorresponding image clarity evaluation value H from the RGB value of theproofing-target pixel, the corresponding image clarity evaluation valueH need only be obtained from the image clarity evaluation value LUT 209using the RGB value as an index. Note that if the RGB value cannot bedirectly referred to in the image clarity evaluation value LUT 209, animage clarity evaluation value H can be obtained using an interpolationmethod such as tetrahedral interpolation. Note also that individualimage clarity evaluation values H are measured in advance for sets ofgonio-spectral reflection characteristics BRDF held in therepresentative color gonio-spectral reflection characteristics holdingunit 204.

In step S1202, the vector R in the specular reflection directionobtained by the virtual environment information obtaining unit 211 instep S1005 is obtained.

In step S1203, a position on the representative color capture intensitymap is determined using the vector R, and a corresponding representativecolor capture intensity map is interpolated, to calculate a captureintensity LUM_(est)(x,y) of the virtual illumination 402 for a targetcolor. This calculation is done in accordance with:

$\begin{matrix}{{{LUM}_{est}\left( {x,y} \right)} = {{\frac{H - H_{\min}}{H_{\max} - H_{\min}} \times {{LUM}_{\max}\left( {x,y} \right)}} + {\frac{H_{\max} - H}{H_{\max} - H_{\min}} \times {{LUM}_{\min}\left( {x,y} \right)}}}} & (7)\end{matrix}$where (x,y) are the coordinates of an intersection point between thespecular reflection vector R at the pixel position n obtained in stepS1202 and a virtual plane (observation illumination image) on which thevirtual illumination 402 is set, and indicates a position on therepresentative color capture intensity map corresponding to the pixelposition n in the proofing-target image; and H_(max) and H_(min) are themaximum and minimum image clarity evaluation values H, respectively,under observation illumination, which are held in the image clarityevaluation value LUT 209; and LUM_(max) and LUM_(min) are therepresentative color capture intensity maps when the image clarityevaluation value H maximizes and minimizes, respectively.

Proofing Color Calculation Processing (S1007)

Proofed image generation processing in step S1007 will be described indetail below with reference to a flowchart shown in FIG. 13.

First, in step S1301, a non-gloss/diffusion XYZ value corresponding tothe RGB pixel value at the pixel position n in the proofing-target imageis obtained from the diffusion LUT 208. In step S1302, a gloss/diffusionXYZ value corresponding to the RGB pixel value at the pixel position nin the proofing-target image is similarly obtained from the gloss LUT207.

FIGS. 14A and 14B illustrate examples of the diffusion LUT 208 and thegloss LUT 207, respectively. As shown in FIG. 14A, the diffusion LUT 208serves as a diffuse component holding unit which holds data measuredwithout capturing observation illumination on, for example, an actualprint product (sample print product) of a sample image having aplurality of RGB color patches. That is, diffuse reflection is measuredin advance for a representative RGB value on the sample print product,and an obtained XYZ value (X_(diff), Y_(diff), Z_(diff)) is held as adiffuse component. This diffuse component is a color component ofreflected light when observation illumination is not captured. Also, asshown in FIG. 14B, the gloss LUT 207 serves as a glossy componentholding unit which holds data measured upon capturing observationillumination (virtual illumination 402) in the above-mentioned sampleprint product. That is, specular reflection of observation illuminationis measured in advance for a representative RGB value on the sampleprint product, and an obtained XYZ value (X_(spec), Y_(spec), Z_(spec))is held as a glossy component. The thus measured glossy component isadded to the diffuse component upon capturing observation illumination.Hence, to look up the diffusion LUT 208 and gloss LUT 207 based on theinput RGB pixel value of the proofing-target image, a correspondingnon-gloss/diffusion XYZ value and gloss/diffusion XYZ value need only beobtained using the RGB value as an index. However, if the diffusion LUT208 or gloss LUT 207 has no RGB value to be processed, this value may becalculated by interpolation such as tetrahedral interpolation. Any printproduct of an image including a plurality of colors is applicable to theabove-mentioned sample print product.

In step S1303, the capture intensity LUM_(est)(x,y) calculated in stepS1203 is obtained. In step S1304, a proofing glossy component at eachXYZ value is calculated from the non-gloss/diffusion XYZ value, thegloss/diffusion XYZ value, and the capture intensity LUM_(est)(x,y), inaccordance with:X _(gloss)=(X _(spec) −X _(diff))×LUM_(est)(x,y)Y _(gloss)=(Y _(spec) −Y _(diff))×LUM_(est)(x,y)Z _(gloss)=(Z _(spec) −Z _(diff))×LUM_(est)(x,y)  (8)

In step S1305, the proofing glossy component and the diffuse componentare combined in accordance with:X _(out) =X _(gloss) +X _(diff)Y _(out) =Y _(gloss) +Y _(diff)Z _(out) =Z _(gloss) +Z _(diff)  (9)to calculate a proofing color corresponding to each pixel value in theproofing-target image, and the process ends.

According to equations (8) and (9), in this embodiment, for each pixelin the proofing-target image, a proofing glossy component is calculatedby multiplying the difference between the glossy component and thediffuse component by the capture intensity, and the diffuse component isadded to the proofing glossy component, to calculate a proofing color.

As described above, according to the first embodiment, first, anillumination capture intensity map for a representative color iscalculated using a lowpass filter based on the gonio-spectral reflectioncharacteristics of the representative color. The representative colorcapture intensity map is interpolated in accordance with the specularreflection vector for a virtual line of sight to calculate a captureintensity at each pixel position in the proofing-target image tocalculate a glossy component using this capture intensity. This makes itpossible to accurately reproduce color-specific glossy components.

Second Embodiment

The second embodiment according to the present invention will bedescribed below. The system configuration of an image processingapparatus in the second embodiment is the same as in the above-mentionedfirst embodiment, and a description thereof will not be given. Note thatespecially parts different from those in the first embodiment will bedescribed hereinafter.

Overview of Image Processing

FIG. 15 is a block diagram showing image processing executed by theimage processing apparatus in the second embodiment and the flow ofdata, that is, a functional configuration in this embodiment. Referringto FIG. 15, an illumination intensity distribution holding unit 501 andvirtual environment generation unit 502 perform the same processing asin the illumination intensity distribution holding unit 201 and virtualenvironment generation unit 202, respectively, shown in FIG. 2, havingbeen described in the above-mentioned first embodiment.

A target color gonio-spectral reflection characteristics calculationunit 508 calculates the gonio-spectral reflection characteristics of atarget color using the gonio-spectral reflection characteristics of arepresentative color obtained from a representative color gonio-spectralreflection characteristics holding unit 507, and an image clarityevaluation value obtained from an image clarity evaluation value LUT506. A target color lowpass filter generation unit 509 generates alowpass filter of the target color based on virtual environmentinformation and the gonio-spectral reflection characteristics of thetarget color calculated by the target color gonio-spectral reflectioncharacteristics calculation unit 508.

A virtual environment information obtaining unit 511 obtains specularreflection vector information in the virtual line-of-sight direction,which is calculated based on virtual environment information designatedvia an input unit 101 by the user. A capture intensity calculation unit510 performs filter processing for the intensity distribution(observation illumination image) of observation illumination under thevirtual environment, generated by the virtual environment generationunit 502, using the target color lowpass filter and the vectorinformation.

Note that a proofing-target image holding unit 503, gloss LUT 504,diffusion LUT 505, proofing color calculation unit 512, proofed imageholding unit 513, and display image generation unit 514 perform the sameprocessing as in the first embodiment, and a description thereof willnot be given.

Details of Image Processing

Image processing in the second embodiment will be described in detailbelow with reference to a flowchart shown in FIG. 16.

First, in step S2001, the virtual environment generation unit 502generates an environment, used to virtually observe a print product, bymeans of CG, as in the first embodiment. In step S2002, theproofing-target image holding unit 503 obtains the RGB value at a pixelposition n in a proofing-target image. The RGB value at the pixelposition n in the proofing-target image will be referred to as a targetcolor hereinafter. In step S2003, the target color gonio-spectralreflection characteristics calculation unit 508 calculatesgonio-spectral reflection characteristics corresponding to the targetcolor obtained in step S2002. Processing in step S2003 will be describedin more detail later.

In step S2004, the target color lowpass filter generation unit 509generates a lowpass filter of the target color based on thegonio-spectral reflection characteristics of the target color calculatedin step S2003, and the virtual environment information generated in stepS2001. The lowpass filter generation method is the same as in the firstembodiment; a distance Dis between virtual illumination and a virtualprint product is obtained, and each emergent angle θ of gonio-spectralreflection characteristics BRDF(θ) is converted into a pixel Pix, togenerate a two-dimensional low-pass filter based on the convertedgonio-spectral reflection characteristics BRDF(Pix).

In step S2005, the virtual environment information obtaining unit 511obtains position information regarding the virtual print productcorresponding to the pixel position n in the proofing-target image inaccordance with a user instruction, to calculate a specular reflectionvector R for a line-of-sight vector E under the virtual environment, asin the first embodiment.

In step S2006, the capture intensity calculation unit 510 performs aconvolution operation based on equation (5), that is, filter processingwhich uses the lowpass filter, as in the first embodiment, for theobservation illumination image, using the coordinates of an intersectionpoint of the specular reflection vector R and the observationillumination image as its center.

In step S2007, the proofing color calculation unit 512 calculates aproofing color for the target color calculated in step S2006, using thegloss LUT 504, the diffusion LUT 505, and the capture intensity for thistarget color, as in the first embodiment. In step S2008, it isdetermined whether proofing color calculation is complete uponprocessing in steps S2002 to S2007 for the total number of pixels N inthe proofing-target image. If n=N, the process advances to step S2010;or if n≠N, the process advances to step S2009, in which the pixelposition n is incremented, and the process returns to step S2002.

In steps S2010 and S2011, the proofing color calculation unit 512 anddisplay image generation unit 514 generate a display image of a proofedimage formed from the proofing color calculated in step S2007, for allpixels in the proofing-target image, as in the first embodiment.

Target Color Gonio-Spectral Reflection Characteristics CalculationProcessing (S2003)

Target color gonio-spectral reflection characteristics calculationprocessing in step S2003 will be described in detail below withreference to a flowchart shown in FIG. 17.

First, in step S2101, an image clarity evaluation value H correspondingto the target color (RGB value) is obtained from the image clarityevaluation value LUT 506, as in the first embodiment. In step S2102,gonio-spectral reflection characteristics are obtained for allrepresentative colors from the representative color gonio-spectralreflection characteristics holding unit 507. In this case, colors havingthe maximum and minimum image clarity evaluation values H, respectively,are used as representative colors, and gonio-spectral reflectioncharacteristics BRDF_(max) and BRDF_(min) are obtained for each of theserepresentative colors.

In step S2103, gonio-spectral reflection characteristics BRDF of therepresentative color are calculated in accordance with:

$\begin{matrix}{{BRDF} = {{\frac{H - H_{\min}}{H_{\max} - H_{\min}} \times {BRDF}_{\max}} + {\frac{H_{\max} - H}{H_{\max} - H_{\min}} \times {BRDF}_{\max}}}} & (10)\end{matrix}$where H_(max) and H_(min) are the maximum and minimum image clarityevaluation values, respectively, and the process ends.

As described above, according to the second embodiment, thegonio-spectral reflection characteristics of a target color arecalculated based on the gonio-spectral reflection characteristics of arepresentative color, and a capture intensity on a virtual print productis calculated using a lowpass filter for the target color, which isgenerated based on the obtained gonio-spectral reflectioncharacteristics. A glossy component is calculated using the obtainedcapture intensity for each pixel in a proofing-target image, therebymaking it possible to more accurately reproduce color-specific glossycomponents, as in the first embodiment.

Third Embodiment

The third embodiment according to the present invention will bedescribed below. The system configuration of an image processingapparatus in the third embodiment is the same as in the above-mentionedfirst embodiment, and a description thereof will not be given. Note thatespecially parts different from those in the first embodiment will bedescribed hereinafter.

Overview of Image Processing

FIG. 18 is a block diagram showing image processing executed by theimage processing apparatus in the third embodiment and the flow of data,that is, a functional configuration in this embodiment. Referring toFIG. 18, in contrast to the configuration shown in FIG. 2 in theabove-mentioned first embodiment, a representative color captureintensity map LUT generation unit 605 is provided in place of therepresentative color capture intensity map calculation unit 205, and theimage clarity evaluation value LUT 209 is omitted. Other configurationsare the same as in the first embodiment, and a description thereof willnot be given.

The representative color capture intensity map LUT generation unit 605generates an LUT which associates an input pixel value and captureintensity map of a representative color with each other, using virtualillumination generated by a virtual environment generation unit 602, anda lowpass filter generated by a representative color lowpass filtergeneration unit 603. Although an RGB value is used as an input pixelvalue for a representative color in the third embodiment, the inputpixel value is not limited to an RGB value as long as it indicates therepresentative color, and an XYZ value or an ink value may be used. Acapture intensity calculation unit 610 calculates a capture intensityfor a target color (RGB value) using the representative color captureintensity map LUT, and a proofing-target pixel value obtained by aproofing-target image holding unit 606.

Details of Image Processing

Image processing in the third embodiment will be described in detailbelow with reference to a flowchart shown in FIG. 19.

First, in step S3001, the virtual environment generation unit 602generates an environment, used to virtually observe a print product, bymeans of CG, as in the first embodiment. In step S3002, therepresentative color lowpass filter generation unit 603 generates alowpass filter for a representative color, as in the first embodiment.More specifically, gonio-spectral reflection characteristics BRDF(θ) ofa representative color are read from a representative colorgonio-spectral reflection characteristics holding unit 604. Therepresentative color in the third embodiment is a color obtained bydividing all color regions in a proofing-target image at a predeterminedinterval. Although the following description assumes 125 colors obtainedby dividing each RGB value into five slices as representative colors,the number of colors is not limited to this, and 729 colors obtained bydividing each RGB value into nine slices, for example, may be used.Next, each emergent angle θ of the gonio-spectral reflectioncharacteristics BRDF(θ) is converted into a pixel Pix for eachrepresentative color, to generate a two-dimensional lowpass filter basedon the converted gonio-spectral reflection characteristics BRDF(Pix).

In step S3003, first, the representative color capture intensity map LUTgeneration unit 605 performs filter processing based on equation (5), asin the first embodiment, for virtual illumination 402 set in a virtualenvironment, for each lowpass filter of the representative colorgenerated in step S3002. Thus, a representative color capture intensitymap of each representative color is generated. Next, a table (LUT)indicating the correspondence between the RGB value and representativecolor capture intensity map of each color is generated. FIG. 20illustrates an example of the representative color capture intensity mapLUT generated in the third embodiment. Referring to FIG. 20, x and y arecoordinates indicating the position in an illumination image formed bythe virtual illumination 402, and take integers of 0 to 1024 when thisillumination image has, for example, 1024×1024 pixels; and LUM_(k)(x,y)is the capture intensity for the kth representative color at theposition (x,y).

In step S3004, the proofing-target image holding unit 606 obtains theRGB value at a pixel position n in the proofing-target image. In stepS3005, the capture intensity calculation unit 610 obtains a captureintensity LUM, of the virtual illumination 402 corresponding to thepixel position n in the proofing-target image and its RGB value, usingthe LUT generated in step S3003. Note that if the RGB value cannot bedirectly referred to in the representative color capture intensity mapLUT, a capture intensity LUM_(est) can be calculated using aninterpolation method such as tetrahedral interpolation.

In step S3006, a proofing color calculation unit 612 calculates aproofing color for the RGB value, calculated in step S3005, using agloss LUT 607, a diffusion LUT 608, and this RGB value, as in the firstembodiment. In step S3007, it is determined whether proofing colorcalculation is complete upon processing in steps S3004 to S3006 for thetotal number of pixels N in the proofing-target image. If n=N, theprocess advances to step S3009; or if n≠N, the process advances to stepS3008, in which the pixel position n is incremented, and the processreturns to step S3004.

In step S3009, the proofing color calculation unit 612 stores a proofedimage in a proofed image holding unit 613. In step S3010, a virtualenvironment information obtaining unit 611 obtains a position on thevirtual print product in accordance with a user instruction, tocalculate a specular reflection vector R for a line-of-sight vector Eunder the virtual environment, as in the first embodiment. In stepS3011, a display image generation unit 614 converts the proofed imageinto a display image in accordance with the specular reflection vectorR, as in the first embodiment, and the process ends.

As described above, according to the third embodiment, a captureintensity map for a representative color is calculated using a lowpassfilter based on the gonio-spectral reflection characteristics of therepresentative color to generate an LUT indicating the correspondencebetween this map and the RGB value of the representative color. Acapture intensity for each pixel position in a proofing-target image andits RGB value are calculated using this LUT to calculate a glossycomponent using this capture intensity, thereby making it possible tomore accurately reproduce color-specific glossy components, as in theabove-mentioned first embodiment.

Other Embodiments

Although an example in which two colors having the maximum and minimumimage clarity evaluation values H, respectively, are used asrepresentative colors has been given in the above-mentioned firstembodiment, the representative colors are not limited to two colors aslong as interpolation is performed using a plurality of representativecolors. For example, it is also possible to use a maximum value H_(max),minimum value H_(min), and intermediate value H_(mid) of an imageclarity evaluation value H to perform interpolation as presented in:

$\begin{matrix}{\mspace{79mu}{{{{When}\mspace{14mu} H_{\min}} \leq H < {H_{mid}\text{:}}}{{{LUM}_{est}\left( {x,y} \right)} = {{\frac{H - H_{\min}}{H_{mid} - H_{\min}} \times {{LUM}_{mid}\left( {x,y} \right)}} + {\frac{H_{mid} - H}{H_{mid} - H_{\min}} \times {{LUM}_{\min}\left( {x,y} \right)}}}}}} & (11) \\{\mspace{56mu}{{{{When}\mspace{14mu} H_{mid}} \leq H < {H_{\max}\text{:}}}{{{LUM}_{est}\left( {x,y} \right)} = {{\frac{H - H_{mid}}{H_{\max} - H_{mid}} \times {{LUM}_{\max}\left( {x,y} \right)}} + {\frac{H_{\max} - H}{H_{\max} - H_{mid}} \times {{LUM}_{mid}\left( {x,y} \right)}}}}}} & (12)\end{matrix}$

Also, an example in which capture intensity for a representative coloris linearly interpolated in accordance with the image clarity evaluationvalue when calculating a capture intensity for a target color has beengiven in the above-mentioned first to third embodiments. However,nonlinear interpolation such as third-order spline interpolation is alsoapplicable in place of linear interpolation as long as interpolation isperformed using a plurality of representative colors.

Aspects of the present invention can also be realized by a computer of asystem or apparatus (or devices such as a CPU or MPU) that reads out andexecutes a program recorded on a memory device to perform the functionsof the above-described embodiment(s), and by a method, the steps ofwhich are performed by a computer of a system or apparatus by, forexample, reading out and executing a program recorded on a memory deviceto perform the functions of the above-described embodiment(s). For thispurpose, the program is provided to the computer for example via anetwork or from a recording medium of various types serving as thememory device (for example, computer-readable medium).

While the present invention has been described with reference toexemplary embodiments, it is to be understood that the invention is notlimited to the disclosed exemplary embodiments. The scope of thefollowing claims is to be accorded the broadest interpretation so as toencompass all such modifications and equivalent structures andfunctions.

This application claims the benefit of Japanese Patent Application No.2010-179004, filed Aug. 9, 2010 which is hereby incorporated byreference herein in its entirety.

What is claimed is:
 1. An image processing apparatus which performs softproof processing of reproducing, on a monitor, a print product underobservation illumination, comprising: a characteristics holding unitconfigured to hold specular reflection of the observation illuminationat a sample print product having a plurality of colors printed thereonas color-specific glossy components, diffuse reflection of theobservation illumination at the sample print product as color-specificdiffuse components, and color-specific gonio-spectral reflectioncharacteristics at the sample print product; an evaluation value holdingunit configured to hold an evaluation value for each of the heldcolor-specific gonio-spectral reflection characteristics; an intensitydistribution holding unit configured to hold an intensity distributionof the observation illumination; a lowpass filter generation unitconfigured to obtain the gonio-spectral reflection characteristics foreach of a plurality of representative colors from said characteristicsholding unit, and to generate a lowpass filter corresponding to thegonio-spectral reflection characteristics for each of the representativecolors, the representative colors corresponding to the evaluation valuesheld in said evaluation value holding unit; a map generation unitconfigured to perform filter processing on the intensity distribution ofthe observation illumination held in said intensity distribution holdingunit, for each of the representative colors, by means of the lowpassfilter corresponding to the representative color, in order to generate amap indicating a capture intensity distribution of observationillumination for the representative color; an intensity calculation unitconfigured to obtain an evaluation value corresponding to a color of apixel in a proofing-target image from said evaluation value holdingunit, and to interpolate a capture intensity at a position correspondingto the pixel in the map for each of the representative colors, based onthe evaluation value, in order to calculate a capture intensity of theobservation illumination for the pixel, for each pixel in theproofing-target image; a proofing color calculation unit configured toobtain the glossy component and the diffuse component corresponding to acolor of a pixel in the proofing-target image from said characteristicsholding unit, to multiply the difference between the glossy componentand the diffuse component by the capture intensity to calculate aproofing glossy component, and to add the diffuse component to theproofing glossy component in order to calculate a proofing color of thepixel, for each pixel in the proofing-target image; and a display imagegeneration unit configured to convert the proofing color into a signalvalue for the monitor to generate a display image, for each pixel in theproofing-target image.
 2. The apparatus according to claim 1, furthercomprising: a virtual environment generation unit configured to generatea virtual environment, used to observe a print product of theproofing-target image, in accordance with the intensity distribution ofthe observation illumination held in said intensity distribution holdingunit; and an obtaining unit configured to obtain a specular reflectionvector for a line-of-sight vector under the virtual environment, foreach pixel in the proofing-target image, wherein said intensitycalculation unit determines a position corresponding to a pixel in theproofing-target image in the map for each of the representative colors,based on the specular reflection vector.
 3. The apparatus according toclaim 1, wherein the evaluation value includes a full width at halfmaximum of the gonio-spectral reflection characteristics function whichindicates a variance of the gonio-spectral reflection characteristics.4. An image processing apparatus which generates a proofed image used toreproduce, on a monitor, a print product under observation illumination,comprising: a characteristics holding unit configured to hold specularreflection of the observation illumination at a sample print producthaving a plurality of colors printed thereon as color-specific glossycomponents, diffuse reflection of the observation illumination at thesample print product as color-specific diffuse components, andcolor-specific gonio-spectral reflection characteristics at the sampleprint product; an evaluation value holding unit configured to hold anevaluation value for each of the held color-specific gonio-spectralreflection characteristics; an intensity distribution holding unitconfigured to hold an intensity distribution of the observationillumination; a characteristics calculation unit configured to obtainthe gonio-spectral reflection characteristics for each of a plurality ofrepresentative colors from said gloss characteristics holding unit, andto calculate gonio-spectral reflection characteristics for a color ofeach pixel in the proofing-target image by means of obtaining theevaluation value corresponding to the color of the pixel from saidevaluation value holding unit, and interpolating the gonio-spectralreflection characteristics for each of the representative colors, basedon the evaluation value, the representative colors corresponding to theevaluation values held in said evaluation value holding unit a lowpassfilter generation unit configured to generate a lowpass filtercorresponding to the gonio-spectral reflection characteristics for acolor of each pixel in the proofing-target image; an intensitycalculation unit configured to perform filter processing on theintensity distribution of the observation illumination held in saidillumination intensity distribution holding unit, by means of thelowpass filter corresponding to a color of a pixel in theproofing-target image, in order to calculate a capture intensitydistribution of the observation illumination for the pixel, for eachpixel in the proofing-target image; a proofing color calculation unitconfigured to obtain the glossy component and the diffuse componentcorresponding to a color of a pixel in the proofing-target image fromsaid characteristics holding unit, to multiply the difference betweenthe glossy component and the diffuse component by the capture intensityto calculate a proofing glossy component, and to add the diffusecomponent to the proofing glossy component in order to calculate aproofing color of the pixel, for each pixel in the proofing-targetimage; and a display image generation unit configured to convert theproofing color into a signal value for the monitor to generate a displayimage, for each pixel in the proofing-target image.
 5. The apparatusaccording to claim 4, further comprising: a virtual environmentgeneration unit configured to generate a virtual environment, used toobserve a print product of the proofing-target image, in accordance withthe intensity distribution of the observation illumination held in saidintensity distribution holding unit; and an obtaining unit configured toobtain a specular reflection vector for a line-of-sight vector under thevirtual environment, for each pixel in the proofing-target image,wherein said intensity calculation unit determines a position, in theintensity distribution of the observation illumination held in saidintensity distribution holding unit, corresponding to a pixel in theproofing-target image, based on the specular reflection vector.
 6. Theapparatus according to claim 4, wherein the evaluation value includes afull width at half maximum of the gonio-spectral reflectioncharacteristics function which indicates a variance of thegonio-spectral reflection characteristics.
 7. An image processingapparatus which generates a proofed image used to reproduce, on amonitor, a print product under observation illumination, comprising: acharacteristics holding unit configured to hold specular reflection ofthe observation illumination at a sample print product having aplurality of colors printed thereon as color-specific glossy components,diffuse reflection of the observation illumination at the sample printproduct as color-specific diffuse components, and color-specificgonio-spectral reflection characteristics at the sample print product;an intensity distribution holding unit configured to hold an intensitydistribution of the observation illumination; a lowpass filtergeneration unit configured to obtain the gonio-spectral reflectioncharacteristics for each of a plurality of representative colors fromsaid gloss characteristics holding unit, and to generate a lowpassfilter corresponding to the gonio-spectral reflection characteristics,where the representative colors include a plurality of colors in a colorregion of a proofing-target image; a map generation unit configured toperform filter processing on the intensity distribution of theobservation illumination held in said intensity distribution holdingunit by means of the lowpass filter, in order to generate a mapindicating a capture intensity distribution of observation illuminationfor the representative color, and to generate a table indicating arelationship between the representative color and the map, for each ofthe representative colors; an intensity calculation unit configured tocalculate a capture intensity of the observation illuminationcorresponding to a position and a color of a pixel in theproofing-target image by accessing up the table, for each pixel in theproofing-target image; a proofing color calculation unit configured toobtain the glossy component and the diffuse component corresponding to acolor of a pixel in the proofing-target image from said characteristicsholding unit, to multiply the difference between the glossy componentand the diffuse component by the capture intensity to calculate aproofing glossy component, and to add the diffuse component to theproofing glossy component in order to calculate a proofing color of thepixel, for each pixel in the proofing-target image; and a display imagegeneration unit configured to convert the proofing color into a signalvalue for the monitor to generate a display image, for each pixel in theproofing-target image.
 8. An image processing method for an imageprocessing apparatus comprising a characteristics holding unit, anevaluation value holding unit, a lowpass filter generation unit, a mapgeneration unit, an intensity calculation unit, a proofing colorcalculation unit, and a display image generation unit, wherein the imageprocessing apparatus performs soft proof processing of reproducing, on amonitor, a print product under observation illumination, wherein: saidcharacteristics holding unit is configured to hold specular reflectionof the observation illumination as color-specific glossy components,diffuse reflection of the observation illumination as color-specificdiffuse components, and color-specific gonio-spectral reflectioncharacteristics; said evaluation value holding unit is configured tohold an evaluation value for each of the held color-specificgonio-spectral reflection characteristics; and said intensitydistribution holding unit is configured to hold an intensitydistribution of the observation illumination; said method comprising: alowpass filter generation step of obtaining the gonio-spectralreflection characteristics for each of a plurality of representativecolors from said characteristics holding unit, and generating a lowpassfilter corresponding to the gonio-spectral reflection characteristicsfor each of the representative colors, the representative colorscorresponding to the evaluation values held in said evaluation valueholding unit; a map generation step of performing filter processing onthe intensity distribution of the observation illumination held in saidintensity distribution holding unit, for each of the representativecolors, by means of the lowpass filter corresponding to therepresentative color, in order to generate a map indicating a captureintensity distribution of observation illumination for therepresentative color; an intensity calculation step of obtaining anevaluation value corresponding to a color of a pixel in aproofing-target image from said evaluation value holding unit, andinterpolating a capture intensity at a position corresponding to thepixel in the map for each of the representative colors, based on theevaluation value, in order to calculate a capture intensity of theobservation illumination for the pixel, for each pixel in theproofing-target image; a proofing color calculation step of obtainingthe glossy component and the diffuse component corresponding to a colorof a pixel in the proofing-target image from said characteristicsholding unit, multiplying the difference between the glossy componentand the diffuse component by the capture intensity to calculate aproofing glossy component, and adding the diffuse component to theproofing glossy component in order to calculate a proofing color of thepixel, for each pixel in the proofing-target image; and a display imagegeneration step of converting the proofing color into a signal value forthe monitor to generate a display image, for each pixel in theproofing-target image.
 9. A non-transitory computer-readable storagemedium storing a program for causing a computer to function as each unitof an image processing apparatus defined in claim 1 by executing theprogram by the computer.